U.S. patent application number 16/010878 was filed with the patent office on 2019-12-19 for manage and control pests infestation using machine learning in conjunction with automated devices.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Michael Bender, Rhonda L. Childress, Stan K. Daley.
Application Number | 20190380325 16/010878 |
Document ID | / |
Family ID | 68838593 |
Filed Date | 2019-12-19 |
United States Patent
Application |
20190380325 |
Kind Code |
A1 |
Bender; Michael ; et
al. |
December 19, 2019 |
MANAGE AND CONTROL PESTS INFESTATION USING MACHINE LEARNING IN
CONJUNCTION WITH AUTOMATED DEVICES
Abstract
Embodiments of the present invention provides a systems and
methods for pest control. The system detects one or more pests
based on receiving sensor data from one or more sensors associated
with a predefined location. The system analyzes the sensor data
with cognitive machine learning based on the detected pests. The
system generates a treatment recommendation report based on the
analysis and outputs the treatment recommendation report.
Inventors: |
Bender; Michael; (Rye Brook,
NY) ; Childress; Rhonda L.; (Austin, TX) ;
Daley; Stan K.; (Atlanta, GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
68838593 |
Appl. No.: |
16/010878 |
Filed: |
June 18, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
A01M 1/106 20130101; A01M 99/00 20130101; A01M 1/20 20130101; G06N
5/04 20130101 |
International
Class: |
A01M 1/10 20060101
A01M001/10; G06N 5/04 20060101 G06N005/04; G06N 99/00 20060101
G06N099/00; A01M 99/00 20060101 A01M099/00; A01M 1/20 20060101
A01M001/20 |
Claims
1. A method for pest control, the method comprising: detecting one
or more pests based on receiving sensor data from one or more
sensors associated with a predefined location; analyzing the sensor
data with cognitive machine learning based on the detected pests;
generating a treatment recommendation report based on the analysis;
and outputting the treatment recommendation report.
2. The method of claim 1, wherein the detecting further comprises:
receiving an auditory pattern based on the one or more pests by the
one or more sensors; determining a pest type based on the received
auditory pattern; responsive to determining the pest type,
identifying the one or more pests; and responsive to not able to
determine the pest type, verifying the one or more pests based on
visual identification from the one or more sensors.
3. The method of claim 2, wherein analyzing the sensor data with
cognitive machine learning based on the detected pests further
comprises: aggregating the one or more sensor data, a reference
data, a first database, and climate data, wherein the first
database comprises historical data; selecting a pest control method
based on the identified one or more pests, wherein the pest control
method comprises at least one of pesticides, traps, and biological
predators; and calculating action plan to apply the pest control
method.
4. The method of claim 3, further comprises: retrieving data from
the first database, wherein the data comprises one or more previous
treatment plans.
5. The method of claim 1, wherein outputting the treatment
recommendation report further comprises: sending the treatment
recommendation report to one or more users and awaiting user
response; responsive to user deciding to ignore the treatment
recommendation report, saving the report to a first database; and
responsive to user deciding perform a pest control method manually
based on the treatment recommendation report, saving the report to
a first database.
6. The method of claim 5, further comprises: responsive to user
deciding to allow automatic treatment, sending an autonomous device
to perform a pest control method based on the treatment
recommendation report.
7. The method of claim 1, wherein the one or more sensors comprise
at least one of a camera, a microphone, or a drone.
8. A computer program product for pest control, the computer
program product comprising: one or more computer readable storage
devices and program instructions stored on the one or more computer
readable storage devices, the stored program instructions
comprising: program instructions to detect one or more pests based
on receiving sensor data from one or more sensors associated with a
predefined location; program instructions to analyze the sensor
data with cognitive machine learning based on the detected pests;
program instructions to generate a treatment recommendation report
based on the analysis; and program instructions to output the
treatment recommendation report.
9. The computer program product of claim 8, wherein program
instructions to detect further comprises: program instructions to
receive an auditory pattern based on the one or more pests by the
one or more sensors; program instructions to determine a pest type
based on the received auditory pattern; responsive to determining
the pest type, program instructions to identify the one or more
pests; and responsive to not able to determine the pest type,
program instructions to verify the one or more pests based on
visual identification from the one or more sensors.
10. The computer program product of claim 9, wherein program
instructions to analyze the sensor data with cognitive machine
learning based on the detected pests further comprises: program
instructions to aggregate the one or more sensor data, a reference
data, a first database, and climate data, wherein the first
database comprises historical data; program instructions to select
a pest control method based on the identified one or more pests,
wherein the pest control method comprises at least one of
pesticides, traps, and biological predators; and program
instructions to calculate action plan to apply the pest control
method.
11. The computer program product of claim 10, the stored program
instructions further comprises: program instructions to retrieve
data from the first database, wherein the data comprises one or
more previous treatment plans.
12. The computer program product of claim 8, wherein program
instructions to output the treatment recommendation report further
comprises: program instructions to send the treatment
recommendation report to one or more users and awaiting user
response; responsive to user deciding to ignore the treatment
recommendation report, program instructions to save the report to a
first database; and responsive to user deciding perform a pest
control method manually based on the treatment recommendation
report, program instructions to save the report to a first
database.
13. The computer program product of claim 12, the stored program
instructions further comprises: responsive to user deciding to
allow automatic treatment, program instructions to send an
autonomous device to perform a pest control method based on the
treatment recommendation report.
14. The computer program product of claim 8, wherein the one or
more sensors comprise at least one of a camera, a microphone, or a
drone.
15. A computer system for pest control, the computer system
comprising: one or more computer processors; one or more computer
readable storage devices; program instructions stored on the one or
more computer readable storage devices for execution by at least
one of the one or more computer processors, the stored program
instructions comprising: program instructions to detect one or more
pests based on receiving sensor data from one or more sensors
associated with a predefined location; program instructions to
analyze the sensor data with cognitive machine learning based on
the detected pests; program instructions to generate a treatment
recommendation report based on the analysis; and program
instructions to output the treatment recommendation report.
16. The computer system of claim 15, wherein program instructions
to detect further comprises: program instructions to receive an
auditory pattern based on the one or more pests by the one or more
sensors; program instructions to determine a pest type based on the
received auditory pattern; responsive to determining the pest type,
program instructions to identify the one or more pests; and
responsive to not able to determine the pest type, program
instructions to verify the one or more pests based on visual
identification from the one or more sensors.
17. The computer system of claim 16, wherein program instructions
to analyze the sensor data with cognitive machine learning based on
the detected pests further comprises: program instructions to
aggregate the one or more sensor data, a reference data, a first
database, and climate data, wherein the first database comprises
historical data; program instructions to select a pest control
method based on the identified one or more pests, wherein the pest
control method comprises at least one of pesticides, traps, and
biological predators; and program instructions to calculate action
plan to apply the pest control method.
18. The computer system of claim 17, the stored program
instructions further comprises: program instructions to retrieve
data from the first database, wherein the data comprises one or
more previous treatment plans.
19. The computer system of claim 15, wherein program instructions
to output the treatment recommendation report further comprises:
program instructions to send the treatment recommendation report to
one or more users and awaiting user response; responsive to user
deciding to ignore the treatment recommendation report, program
instructions to save the report to a first database; and responsive
to user deciding perform a pest control method manually based on
the treatment recommendation report, program instructions to save
the report to a first database.
20. The computer system of claim 15, the stored program
instructions further comprises: responsive to user deciding to
allow automatic treatment, program instructions to send an
autonomous device to perform a pest control method based on the
treatment recommendation report.
Description
BACKGROUND OF THE INVENTION
[0001] The present invention relates generally to the field of pest
control and more particularly to automated pest control.
[0002] Pest and pathogens cost global agriculture $540 billion
dollars a year and termites can cause an additional $5 billion a
year to homeowners. By identifying an infestation early, farmers or
homeowners can take proactive actions (e.g., organic, chemical or
physical) to prevent the pest problem from spreading and ruining
homes and/or crops.
[0003] One of the phase regarding the current method of pest
detection typically involves visual identification and frequent
monitoring. The other phases involves actual treatment of pests.
Regular observation is also critically important. Observation can
broken into inspection and identification steps. Visual inspection,
insect traps, and other methods are used to monitor pest levels. In
addition, record-keeping is also vitality essential. Furthermore,
knowledge target pest behavior, reproductive cycles and ideal
temperature. Sometimes, visual identification of the affected area
can even be too late (i.e., observing brittle wood due to a colony
of termites).
[0004] Therefore, realizing a cognitive system utilizing smart
devices for pest control has a fundamental interest in the
agriculture and pest control industry.
SUMMARY
[0005] According to one embodiment of the present invention, a
method is provided. The method comprising: detecting one or more
pests based on receiving sensor data from one or more sensors
associated with a predefined location; analyzing the sensor data
with cognitive machine learning based on the detected pests;
generating a treatment recommendation report based on the analysis;
and outputting the treatment recommendation report.
[0006] Another embodiment of the present invention, a computer
program product is provided. The computer program product
comprising: one or more computer readable storage devices and
program instructions stored on the one or more computer readable
storage devices, the stored program instructions comprising:
program instructions to detect one or more pests based on receiving
sensor data from one or more sensors associated with a predefined
location; program instructions to analyze the sensor data with
cognitive machine learning based on the detected pests; program
instructions to generate a treatment recommendation report based on
the analysis; and program instructions to output the treatment
recommendation report.
[0007] Another embodiment of the present invention, a computer
system is provided. The computer system comprising: one or more
computer processors; one or more computer readable storage devices;
program instructions stored on the one or more computer readable
storage devices for execution by at least one of the one or more
computer processors, the stored program instructions comprising:
program instructions to detect one or more pests based on receiving
sensor data from one or more sensors associated with a predefined
location; program instructions to analyze the sensor data with
cognitive machine learning based on the detected pests; program
instructions to generate a treatment recommendation report based on
the analysis; and program instructions to output the treatment
recommendation report.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a functional block diagram illustrating the
topology of the host server pest control environment 100, in
accordance with an embodiment of the present invention;
[0009] FIG. 2 is a functional block diagram illustrating the
components of pest control component 111, in accordance with an
embodiment of the present invention;
[0010] FIG. 3 is a flowchart, designated as 300, depicting
operational steps of method for executing the host server pest
control environment 100, in accordance with an embodiment of the
present invention; and
[0011] FIG. 4 depicts a block diagram, designated as 400, of
components of the server computer executing the program within the
host server, in accordance with an embodiment of the present
invention.
DETAILED DESCRIPTION
[0012] Embodiments of the present invention recognize that
improvements to pest control can be made by using machine learning
techniques in conjunction with smart sensors. The invention
leverages IoT (Internet of Things) technology to capture sounds and
images to identify various pests in an environment (e.g., farms,
homes, etc.). After storing the captured inputs, the system uses
machine learning techniques to predict what actions are required
based on historical infestations that have affected the location
previously or similar locations previously. If the infestations are
new then the system will analyze and recommend an action based on
the current data. The system can account for several variables such
as, the time of the year, predicted weather conditions and specific
crops. By using this system, a user can take proactive action
before they would see a problem solely relying on human
tracking.
[0013] Detailed description of embodiments of the claimed
structures and methods are disclosed herein; however, it is to be
understood that the disclosed embodiments are merely illustrative
of the claimed structures and methods that may be embodied in
various forms. In addition, each of the examples given in
connection with the various embodiments is intended to be
illustrative, and not restrictive. Further, the figures are not
necessarily to scale, some features may be exaggerated to show
details of particular components. Therefore, specific structural
and functional details disclosed herein are not to be interpreted
as limiting, but merely as a representative basis for teaching one
skilled in the art to variously employ the methods and structures
of the present disclosure.
[0014] References in the specification to "one embodiment", "an
embodiment", "an example embodiment", etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may not necessarily include
the particular feature, structure, or characteristic. Moreover,
such phrases are not necessarily referring to the same embodiment.
Further, when a particular feature, structure, or characteristic is
described in connection with an embodiment, it is submitted that it
is within the knowledge of one skilled in the art to affect such
feature, structure, or characteristic in connection with other
embodiments, whether or not explicitly described.
[0015] FIG. 1 is a functional block diagram illustrating a host
server pest control environment, generally designated 100, in
accordance with one embodiment of the present invention. FIG. 1
provides only an illustration of one implementation and does not
imply any limitations with regard to the environments in which
different embodiments may be implemented. Many modifications to the
depicted environment may be made by those skilled in the art
without departing from the scope of the invention as recited by the
claims.
[0016] Host server pest control environment 100 includes host
server 110, audio and visual sensors 120, climate server 130, and
reference database server 140, all interconnected over network 103.
Network 103 can be, for example, a telecommunications network, a
local area network (LAN), a wide area network (WAN), such as the
Internet, or a combination of the three, and can include wired,
wireless, or fiber optic connections. Network 103 can include one
or more wired and/or wireless networks that are capable of
receiving and transmitting data, voice, and/or video signals,
including multimedia signals that include voice, data, and video
information. In general, network 103 can be any combination of
connections and protocols that can support communications between
host server 110, audio and visual sensors 120, climate server 130,
reference database server 140, and other computing devices (not
shown) within host server pest control environment 100.
[0017] Host server 110 can be a standalone computing device, a
management server, a web server, a mobile computing device, or any
other electronic device or computing system capable of receiving,
sending, and processing data. In other embodiments, host server 110
can represent a server computing system utilizing multiple
computers as a server system, such as in a cloud computing
environment. In another embodiment, host server 110 can be a laptop
computer, a tablet computer, a netbook computer, a personal
computer (PC), a desktop computer, a personal digital assistant
(PDA), a smart phone, or any other programmable electronic device
capable of communicating with audio and visual sensors 120, and
other computing devices (not shown) within host server pest control
environment 100 via network 103. In another embodiment, host server
110 represents a computing system utilizing clustered computers and
components (e.g., database server computers, application server
computers, etc.) that act as a single pool of seamless resources
when accessed within host server pest control environment 100. Host
server 110 includes pest control component 111 and database
118.
[0018] Pest control component 111 enables the present invention to
manage and control pest infestations. In the depicted embodiment,
pest control component 111 resides on host server 110. In another
embodiment, pest control component 111 can reside on climate server
130, or reference database server 140. In the depicted embodiment,
pest control component 111 consists of several components (refer to
FIG. 2) such as pest detection component 112, target of pest
component 113, data gathering component 114, analysis component
115, and cognitive sub-component component 116.
[0019] Database 118 is a repository for data used by pest control
component 111. In the depicted embodiment, database 118 resides on
host server 110. In another embodiment, database 118 may reside
elsewhere within host server pest control environment provided that
pest control component 111 has access to database 118. A database
is an organized collection of data. Database 118 can be implemented
with any type of storage device capable of storing data and
configuration files that can be accessed and utilized by host
server 110, such as a database server, a hard disk drive, or a
flash memory. Database 118 uses one or more of a plurality of
techniques known in the art to store a plurality of information.
For example, database 118 may store information such as vegetation
information and pest information. In another example, database 118
can contain historical information regarding prior treatment, prior
infestations, prior user actions and prior weather conditions.
[0020] Audio and visual sensors 120 are one or more specialized
devices (e.g., IoT wireless camera, IoT wireless microphone, etc.)
that are capable of detecting and process audio and visual
environment in the vicinity of the devices. In another embodiment,
audio and visual sensors 120 can include autonomous drones equipped
with onboard camera and microphone to aid in identification of
pests.
[0021] Climate server 130 contains weather related information,
such as, but is not limited to, a historical weather pattern, a
current weather condition, and a future weather forecast. Climate
server 130 can be a standalone computing device, a management
server, a web server, a mobile computing device, or any other
electronic device or computing system capable of receiving,
sending, and processing data. In other embodiments, climate server
130 can represent a server computing system utilizing multiple
computers as a server system, such as in a cloud computing
environment. In another embodiment, host server 110 can be a laptop
computer, a tablet computer, a netbook computer, a personal
computer (PC), a desktop computer, a personal digital assistant
(PDA), a smart phone, or any other programmable electronic device
capable of communicating with reference database server 140, and
other computing devices (not shown) within host server pest control
environment 100 via network 103. In another embodiment, climate
server 130 represents a computing system utilizing clustered
computers and components (e.g., database server computers,
application server computers, etc.) that act as a single pool of
seamless resources when accessed within host server pest control
environment 100.
[0022] Reference database server 140 is a server that contains
various reference related information that may help diagnose and
treat pest infestation. For example, reference database server 140
can climate and weather related information for the location of the
users. Reference database server 140 can be a standalone computing
device, a management server, a web server, a mobile computing
device, or any other electronic device or computing system capable
of receiving, sending, and processing data. In other embodiments,
reference database server 140 can represent a server computing
system utilizing multiple computers as a server system, such as in
a cloud computing environment.
[0023] FIG. 2 is a functional block diagram illustrating the
components of pest control component 111, in accordance with an
embodiment of the present invention. Pest control component 111
includes pest detection component 112, target of pest component
113, data gathering component 114 and analysis component 115 along
with cognitive sub-component 116.
[0024] Pest detection component 112 of the present invention
provides the capability of identifying and discerning of pests
based on the received data from audio and visual sensors 120 (e.g.,
IoT wireless camera, IoT wireless microphones, drones, etc.). In an
embodiment, pest detection component 112 can recognize and
differentiate various types of pests nearby the users. For example,
pest detection component 112 identifies a swarm of beetles (e.g.,
Southern Corn Leaf Beatle, Asiatic garden beetle, etc.) known to
target corn fields by the auditory pattern of their wingbeats. In
another example, pest detection component 112 detects a sound of
wood being chewed on an apple tree. However, it is not able to
determine the exact pest type, therefore, pest detection component
112 may send an autonomous drone to investigate and validate the
type of pests.
[0025] Target of pest component 113 of the present invention
provides the capability of identifying and discerning the target of
pest infestation (e.g., livestock, vegetation, etc.) based on the
received data from the various sensors (e.g., IoT wireless camera,
IoT wireless microphones, etc.). In an embodiment, target of pest
component 113 can recognize and differentiate various types of
vegetation (i.e. crops) grown by the users being targeted by pests.
For example, a swarm of Southern Corn Leaf beetles identified by
pest detection component 112 was detected within a vicinity of the
corn field belonging to the user. Target of pest component 113
correctly identifies the target of the beetles, the corn field via
IoT devices.
[0026] In another embodiment, target of pest component 113 can
recognize and differentiate livestock that are targeted by pests.
For example, gnat and flies were identified earlier by pest
detection component 112. However, little is known what the pests
are doing in the vicinity on the farm. An IoT device detects these
pest buzzing around the herd of cattle grazing on the farm.
Therefore, target of pest component 113 is able to correctly
identify the target, the herd of cattle, of the gnats and
flies.
[0027] In yet another embodiment, target of pest component 113 can
recognize and differentiate the target of termites (i.e., wood beam
of the foundation of a house). For example, target of pest
component 113 can identify the type of wood being targeted by
termites. Target of pest component 113 is able to determine the
pine wood frame of the house of the user as the target of termites
based on the information sent by the IoT devices where the device
picked up the sound of termites chewing on the wood.
[0028] Data gathering component 114 of the present invention
provides the capability of retrieving various information (e.g.,
reference data on insects, reference data on the weather, etc.)
from various databases (e.g., reference database server 140, etc.).
In an embodiment, pest detection component 112 can retrieve
vegetation data (i.e., corn) from reference database server 140.
For example, database server 140 can contain general reference data
on the corn crop such as ideal moisture level, ideal growing
temperature, and ideal soil composition. This will further aid pest
control component 111 with identifying issues (e.g., pests,
weather, etc.) that might hinder optimal corn production.
[0029] In an embodiment, pest detection component 112 can retrieve
pest data (i.e., corn beetle) from reference database server 140.
For example, reference database server 140 can contain general
reference data on the corn beetle such as favorite crop/source of
food, life span, and gestation period and habit/behavior pattern.
This will further aid pest control component 111 with identifying
issues that might hinder optimal corn production.
[0030] Analysis component 115 of the present invention provides the
capability of analyzing all received data and determining the
several options for pest treatment. It is noted that analysis
component 115 contains a subcomponent, cognitive sub-component 116.
In an embodiment, pest detection component 112 can recognize a new
pest infestation and immediately determine one or more options for
the user. In another embodiment, pest detection component 112
through cognitive sub-component 116 can recognize an early sign of
pest infestation based on similar situation (i.e., same crops and
pests) from the prior year and make a similar recommendation for
treatment. Additionally, cognitive sub-component 116 can learn
(e.g., machine learning, deep learning, etc.) over time of new,
current and/or prior infestation and take a corrective action. It
is noted that not all recommendation made by analysis component 115
results in an extermination plan for pests. It is possible that no
action is taken based on pre-determined parameters. For example, a
crop loss percentage of 10% is programmed into the system when the
growth of corn is decimated by pests and/or weather, the pest
control system should only take action when the loss is greater
than that 10% threshold. In another example, no action is taken on
a swarm of beetles, targeting wheat, due to the incoming weather
pattern where a cold front is predicted to come through the farm.
The cold front is cold enough to kill the beetles but not cold
enough to hurt the wheat fields.
[0031] FIG. 3 is a flowchart, designated as 300, depicting
operational steps of method for executing the host server pest
control environment 100, in accordance with an embodiment of the
present invention.
[0032] Pest control component 111 detect pest(s) (step 302). In an
embodiment, pest control component 111 through pest detection
component 112 automatically detect the presence of pests based on
audio and visual sensors 120 located throughout the land and/or
dwelling belonging to the users. For example, an IoT microphone
detects sound of a large number of earworm larvae emerging from
their cocoon. In order to confirm the type of larvae, pest control
component 111 through pest detection component 112 can control the
nearest video IoT devices to capture the picture of the larvae for
a visual confirmation and identification. Furthermore, pest control
component 111 through pest detection component 112 can direct
autonomous devices such as drones to investigate the source of the
sound and confirm the type of insects by a visual identification
process. The visual identification method can leverage any current
technology to identify pests.
[0033] After detecting pests, pest control component 111 determines
the target of the pests. In an embodiment, pest control component
111 through target of pest component 113 determine the type of
crops (e.g., agriculture crops, a single rose bush, etc.) affected
by the identified pest from the prior step (step 302). For example,
using audio and visual sensors 120 (i.e., smart devices) located
throughout the property, pest control component 111 through target
of pest component 113 confirms the type of vegetation (i.e., corn)
where the earworm larvae have emerged.
[0034] In another embodiment, pest control component 111 through
target of pest component 113 determine the type of livestock (e.g.,
cattle, pigs, etc.) affected by the identified pest from the prior
step (step 302). For example, using audio and visual sensors 120
(i.e., smart devices) located throughout the property, pest control
component 111 through target of pest component 113 confirms the
type of livestock (i.e., cattle) where the gnats and flies have
been accumulating.
[0035] In yet another embodiment, pest control component 111
through target of pest component 113 determine the type of
livestock (e.g., cattle, pigs, etc.) affected by the identified
pest from the prior step (step 302). For example, using smart dev
audio and visual sensors 120 (i.e., smart devices) located
throughout the property, pest control component 111 through target
of pest component 113 confirms the type of livestock (i.e., cattle)
where the gnats and flies have been accumulating.
[0036] In another embodiment, pest control component 111 does not
determine vegetation type since the identified pest is not
associated with consumption of any vegetation
[0037] In yet another embodiment, target of pest component 113 can
recognize and differentiate the target of termites (i.e. wood). For
example, target of pest component 113 can identify the type of wood
being targeted by termites. Target of pest component 113 is able to
determine the pine wood frame of the house of the user as the
target of termites based on the information sent by the audio and
visual sensors 120 (i.e., smart devices) where the device picked up
the sound of termites chewing on the wood and drones were sent to
validate the location of the infestation. Furthermore, pest control
component 111 can detect and identify a family of pigeons in the
attic of the house of the user. However, there were no property
(i.e., attic eve) destroyed since the pigeons were merely using the
outside nook of the attic for shelter.
[0038] After detecting the pest and target of pests, pest detection
component 112 can retrieve data to help with the decision making
process. In an embodiment, pest control component 111 retrieves
reference data to help perform further analysis on the new (i.e.,
no prior interaction) identified pest. For example, pest control
component 111 retrieves various data (e.g., life span, nesting
habits, source of food, etc.) from reference database server 140
associated with the family of pigeons living in attic.
[0039] Pest control component 111 analyzes data (step 304). In an
embodiment, pest control component 111 can analyze data through
data gathering component 114. Data gathering component 114 can
aggregated data from the following sources, but not limited to,
pest detection component 112, target of pest component 113,
database 118, climate server 130, and reference database server
140.
[0040] Pest control component 111 generate treatment plan (step
306). After aggregating data, pest control component 111 can start
analyzing the data via analysis component 115. The analysis process
can involve several decision selections such as choosing the best
type of pest control method (e.g., biological predator, pesticides,
physical traps, etc.) and the best application of the pest control
method (i.e., human worker to trap or send in drones to drop
pesticides).
[0041] In an embodiment, pest control component through cognitive
sub-component 116 can recall if the detected pests were from
similar interactions. If cognitive sub-component 116 recalls from
database 118 that the infestations were the same then it can
retrieve the treatment plan from previous year. For example, pest
control component 111 determines that the earworm larvae infested
the corn crop last year (i.e., spring time) in the same section of
the farm from database 118. Otherwise, if the situation has changed
such as the weather is different (i.e., freezing spell) and could
possibly interfere with the pests without interaction from pest
detection component 112 then cognitive sub-component 116 can choose
to ignore the previous treatment plan and create a new one based on
new conditions.
[0042] Pest control component outputs treatment plan (step 308). In
an embodiment, after retrieving prior interaction data and
analyzing other current variables (e.g., weather conditions, soil
conditions, etc.) pest control component 111 through analysis
component 115 determines treatment plan and outputs the treatment
recommendation report to the user. Pest control component 111 can
wait for user input regarding the next steps. For example, the user
may tell pest control component 111 to take no further action or
that the user may take action themselves based on the treatment
recommendation report. It is noted that pest control component 111
can bypass the user input and perform pest control based on the
extermination plan by using remote autonomous devices (e.g., drones
with pesticides, etc.) if the user has that feature enabled in the
setting. It is further noted that some identified pests may not
warrant an extermination solution. For example, a single bug was
identified eating a peach. Based on historical data for that crop
and small volume grown, pest control component 111 determines that
no treatment is necessary at the moment and recommends constant
monitoring. It is noted that a treatment plan can be created/output
to the users for each infestations. The user can select the
frequency on receiving the treatment recommendation report or can
set the system to autonomously and automatically exterminate pests
without any user input (i.e. zero treatment plan output). It is
further noted that cognitive sub-component 116 can save all
decisions, analysis and treatment plans in database 118 as part of
the unsupervised learning process (i.e., cognitive learning).
[0043] In another embodiment, pest control component 111 does not
output a treatment plan based on the weather forecast. For example,
a cold front is moving into the area that will kill off the pests
(i.e. corn beetle) but not the crops (i.e. corn). Therefore, pest
control component 111 determines no action is needed regarding the
corn beetle. However, a treatment recommendation report is
generated and sent to the users.
[0044] FIG. 4 depicts a block diagram, designated as 400, of
components of the server computer executing the program within the
host server accelerator environment of FIG. 1, in accordance with
an embodiment of the present invention.
[0045] Host server 110 can include processor(s) 404, cache 416,
memory 406, persistent storage 408, communications unit 410,
input/output (I/O) interface(s) 412 and communications fabric 402.
Communications fabric 402 provides communications between cache
416, memory 406, persistent storage 408, communications unit 410,
and input/output (I/O) interface(s) 412. Communications fabric 402
can be implemented with any architecture designed for passing data
and/or control information between processors (such as
microprocessors, communications and network processors, etc.),
system memory, peripheral devices, and any other hardware
components within a system. For example, communications fabric 402
can be implemented with one or more buses.
[0046] Memory 406 and persistent storage 408 are computer readable
storage media. In this embodiment, memory 406 includes random
access memory (RAM). In general, memory 406 can include any
suitable volatile or non-volatile computer readable storage media.
Cache 416 is a fast memory that enhances the performance of
processor(s) 404 by holding recently accessed data, and data near
recently accessed data, from memory 406.
[0047] Program instructions and data used to practice embodiments
of the present invention, e.g., pest control component 111 and
database 118, can be stored in persistent storage 408 for execution
and/or access by one or more of the respective processor(s) 404 of
host server 110 via memory 406. In this embodiment, persistent
storage 408 includes a magnetic hard disk drive. Alternatively, or
in addition to a magnetic hard disk drive, persistent storage 408
can include a solid-state hard drive, a semiconductor storage
device, a read-only memory (ROM), an erasable programmable
read-only memory (EPROM), a flash memory, or any other computer
readable storage media that is capable of storing program
instructions or digital information.
[0048] The media used by persistent storage 408 may also be
removable. For example, a removable hard drive may be used for
persistent storage 408. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer readable storage medium that is
also part of persistent storage 408.
[0049] Communications unit 410, in these examples, provides for
communications with other data processing systems or devices,
including resources of climate server 130. In these examples,
communications unit 410 includes one or more network interface
cards. Communications unit 410 may provide communications through
the use of either or both physical and wireless communications
links. Pest control component 111 and database 118 may be
downloaded to persistent storage 408 of host server 110 through
communications unit 410.
[0050] I/O interface(s) 412 allows for input and output of data
with other devices that may be connected to host server 110. For
example, I/O interface(s) 412 may provide a connection to external
device(s) 418 such as a keyboard, a keypad, a touch screen, a
microphone, a digital camera, and/or some other suitable input
device. External device(s) 418 can also include portable computer
readable storage media such as, for example, thumb drives, portable
optical or magnetic disks, and memory cards. Software and data used
to practice embodiments of the present invention, e.g., pest
control component 111 and database 118 on host server 110, can be
stored on such portable computer readable storage media and can be
loaded onto persistent storage 408 via I/O interface(s) 412. I/O
interface(s) 412 also connect to a display 420.
[0051] Display 420 provides a mechanism to display data to a user
and may be, for example, a computer monitor or the lenses of a head
mounted display. Display 420 can also function as a touchscreen,
such as a display of a tablet computer.
[0052] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0053] The computer readable storage medium can be any tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0054] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0055] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0056] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0057] These computer readable program instructions may be provided
to a processor of a general purpose computer, a special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0058] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0059] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, a segment, or a portion of instructions, which comprises
one or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0060] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the invention. The terminology used herein was chosen
to best explain the principles of the embodiment, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
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